Summary of Gabinsight: Exploring Gender-activity Binding Bias in Vision-language Models, by Ali Abdollahi et al.
GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models
by Ali Abdollahi, Mahdi Ghaznavi, Mohammad Reza Karimi Nejad, Arash Mari Oriyad, Reza Abbasi, Ali Salesi, Melika Behjati, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper examines the biases present in vision-language models (VLMs) when identifying individuals performing various activities in images. Specifically, it investigates how VLMs are biased towards associating a particular gender with an activity, which is referred to as Gender-Activity Binding (GAB) bias. The researchers introduce the GAB dataset, comprising approximately 5500 AI-generated images that represent diverse activities, and evaluate the performance of 12 pre-trained VLMs on this dataset in text-to-image and image-to-text retrieval tasks. The results indicate a significant average performance decline of about 13.2% when VLMs are confronted with gender-activity binding bias. This research highlights the importance of addressing these biases to ensure more accurate and unbiased predictions in various downstream applications. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computer models that analyze images and text can be biased towards showing certain people doing certain things. The researchers found that these models are often wrong when it comes to identifying who is doing what, especially if the image shows someone doing something that doesn’t match common stereotypes. They created a special dataset of images with different activities to test how well these models do in real-life scenarios. They found that the models did significantly worse when they were shown biased information, which is an important finding for making sure our computer models are fair and accurate. |